✍️ Text Generation with GPT Models — विस्तृत मार्गदर्शिका (Hindi)

यह लेख GPT शैली के Generative Language Models के उपयोग और अंदरूनी कामकाज का एक व्यापक, व्यावहारिक और सिद्धांत-आधारित मार्गदर्शक है। हम चर्चा करेंगे कि इन मॉडलों का internal architecture कैसे काम करता है, pretraining और fine-tuning की रणनीतियाँ, decoding and sampling methods, prompt engineering की बेहतरीन प्रथाएँ, production deployment patterns, hallucination mitigation, evaluation metrics और safety considerations। लेख का उद्देश्य है कि आप production-grade text generation systems डिजाइन और चलाने के लिए सक्षम बनें।

1. संक्षेप में: GPT क्या हैं?

GPT का पूरा नाम है Generative Pre-trained Transformer. ये decoder-only Transformer architectures होते हैं जो autoregressive तरीके से language generate करते हैं। उन्हें बड़े पैमाने पर unsupervised टेक्स्ट corpora पर pre-train किया जाता है ताकि वे syntax, semantics और world knowledge का statistical मॉडल सीख सकें। GPT मॉडल लगातार एक token के बाद अगला token अनुमानित करते हैं और इस प्रक्रिया को जारी रखकर coherent पाठ बनाते हैं।

1.1 प्रमुख घटक

• Tokenization (BPE / SentencePiece / WordPiece)
• Self-Attention और Multi-head Attention
• Positional Encodings (learned या sinusoidal)
• Feed-forward layers, Layer Normalization, residual connections
• Language modeling objective: next-token prediction

2. Tokenization और text representation

Text को tokens में तोड़ना LLM की शुरुआत है। Subword tokenizers vocabulary को manageable रखते हैं और rare words को subword sequences में बदल देते हैं। टोकनाइज़र का चुनाव latency, multilingual support और vocabulary coverage पर प्रभाव डालता है।

आधुनिक pipelines में अक्सर SentencePiece या BPE का प्रयोग होता है, और multilingual models के लिए unigram or SentencePiece-based tokenization बेहतर results देते हैं। Tokenization के प्रभाव नीचे आते हैं: token count (cost), sequence length limits (context window) और tokenization artifacts (word-splitting affecting semantics)।

3. Transformer internals (short technical recap)

Transformer block का core component self-attention है। एक token अपने context के अन्य tokens के keys और values के साथ similarity compute करके weighted sum बनाता है। Multi-head attention अलग-अलग subspaces में context देखता है। positional encoding sequence order जानकारी देता है।

# Pseudocode: attention score (conceptual)
Q = X W_q    # queries
K = X W_k    # keys
V = X W_v    # values
scores = softmax( (Q K^T) / sqrt(d_k) )
attention = scores V
    

4. Pre-training: objective और data

Pre-training में objective सामान्यतः next-token prediction होता है: maximize log probability of next token given previous context. Data pipeline में deduplication, filtering, quality checks और mixture of domains (web text, books, code, dialogues) शामिल होते हैं। Data quality model behavior और hallucination tendencies पर बड़ा असर डालती है।

4.1 Data engineering का महत्व

• Deduplication से memorization कम होता है
• Filtering से toxicity घटती है
• Domain-balanced sampling से specialized knowledge बढ़ती है

5. Fine-tuning और parameter-efficient strategies

Pre-trained LLMs को downstream tasks पर adapt करने के कई तरीके हैं:

• Full fine-tuning: पूरे मॉडल को labeled data पर train करना (compute-heavy)
• Adapter layers: छोटे dense layers insert कर adapter weights train करना
• LoRA (Low-Rank Adaptation): weight updates को low-rank parameterization में restrict करना
• Prompt tuning / Prefix tuning: input-space या prompt-layer based adaptation
• Instruction tuning: human-written instruction-response pairs पर supervised tuning

5.1 LoRA की एक छोटी रूपरेखा

LoRA base weight W को W + ΔW के रूप में parameterize करता है और ΔW = A B (low-rank) को train करता है। इससे GPU मेमरी बचती है और adapters shared रहती हैं।

6. Decoding strategies — creativity vs reliability

Decoding यानी generated token sequence बनाना critical step है। चयनित decoding strategy output की creativity, coherence और factuality प्रभावित करती है.

6.1 Greedy decoding

हर step पर सबसे probable token चुनना। तेज और deterministic पर कभी-कभी repetitive और bland होता है।

6.2 Beam search

Multiple hypotheses track करता है और global score maximize करता है। Translation जैसे tasks में उपयोगी पर diversity कम कर सकता है।

6.3 Sampling: temperature, top-k, top-p

Sampling randomness introduce करती है। Temperature scaling logits को soften/ sharpen करता है. Top-k restricts vocabulary to top k tokens; Top-p (nucleus) selects smallest set of tokens whose cumulative probability >= p. Modern practice often uses top-p with temperature for balance.

# Example: decoding arguments (Hugging Face)
outputs = model.generate(
  inputs,
  do_sample=True,
  max_new_tokens=150,
  temperature=0.8,
  top_p=0.92,
  top_k=50,
  no_repeat_ngram_size=3
)
    

7. Prompt engineering for GPT — practical patterns

Prompt design quality often has a larger effect than small model size changes. Best practices:

• Be explicit about format (e.g., "Output as JSON with keys title and summary")
• Provide context and examples (few-shot) for structured tasks
• Limit unnecessary context to stay within max context window
• Use system-level instructions (in chat APIs) to set tone and refusal behavior
• Use chain-of-thought only when controlled and when model supports it safely

7.1 Prompt templates — examples

Template: "You are a helpful assistant. Summarize the article below in Hindi in 5 bullets. Article: {article}"
Few-shot template: include 2-3 labeled examples above the task.
Format enforcement: "Output valid JSON matching schema: {"title": string, "bullets": [string]}"
    

8. Reducing hallucination — grounding techniques

Hallucination is one of LLMs biggest practical challenges. Common mitigations:

• Retrieval-Augmented Generation (RAG): retrieve relevant documents and give them as context
• Tool use and verification: call calculators, search engines, or knowledge sources during generation
• Constrain outputs via schemas and validators
• Post-generation verification: fact-check using external sources

8.1 RAG pipeline overview

1. Document ingestion and chunking
2. Embedding index creation (e.g., HNSW)
3. Retriever returns top-k passages for a query
4. Composer builds context and prompts the LLM
5. Generator conditions on retrieved text and produces grounded answer

9. Evaluation metrics for generated text

Evaluation depends on task. Some general metrics:

• Perplexity for intrinsic language modeling
• BLEU/ROUGE for translation and summarization proxies
• EM/F1 for QA extraction
• Human evaluation for fluency, helpfulness and factuality
• Task-specific automated checks and unit tests

9.1 Human evaluation frameworks

Human raters often score outputs on scales (fluency, correctness, relevance, bias). Crowdsourcing plus expert review is common. Create clear annotation guidelines to reduce variance.

10. Practical code examples (Hugging Face + FastAPI)

Example shows minimal generation API (suitable as starting point):

from fastapi import FastAPI, Request
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch

app = FastAPI()

model_name = "gpt2"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
generator = pipeline("text-generation", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)

@app.post("/generate")
async def generate(req: Request):
    data = await req.json()
    prompt = data.get("prompt", "")
    params = data.get("params", {"max_new_tokens": 128, "temperature": 0.8, "top_p": 0.9})
    out = generator(prompt, **params)
    return {"text": out[0]["generated_text"]}
    

Production additions: authentication, rate limiting, batching, caching (for repeated prompts), and safety filters on outputs before returning to user.

11. Cost, latency and scaling considerations

Long contexts and large models increase token compute cost. Strategies:

• Speculative decoding: run smaller model first and confirm with larger model
• Distillation: smaller distilled models for low-latency use-cases
• Quantization and mixed-precision inference
• KV caching and attention optimizations for streaming generation
• Batching requests and asynchronous workers

12. Safety, bias and governance

Deploying text generation systems responsibly requires policy, technical safeguards and monitoring:

• Safety filters and classifiers for toxicity, hate, sexually explicit content
• Prompt restrictions to avoid harmful instructions (no instructions for illegal acts)
• Privacy safeguards: redact PII from ingested context and logs
• Audit logs and versioning of prompts, model weights and policies
• Human escalation and appeal flows for disputed outputs

13. Case study: building a long-form article writer

Example progression to build a production article generator:

1. Collect seed outlines/structures from expert articles
2. Create prompt templates that accept outline + tone + keywords
3. Use iterative generation: outline -> expand sections -> revise
4. Human-in-the-loop editor who finalizes and factchecks
5. Monitor for plagiarism and factual errors, run citation checks

14. Advanced topics and research directions

Research areas improving text generation:

• RAG improvements and better retrievers
• Faithful generation and grounding
• LLM compression and efficient inference
• Multimodal generation (text + image + audio)
• Better evaluation metrics aligning with human judgment

15. Practical checklist before launching a text generation feature

• Define scope and refusal policies
• Design prompt templates and tests
• Implement safety filters and monitoring
• Benchmark latency and cost per 1k tokens
• Set human review flows and rollback plans
• Document model, prompt versions, and dataset provenance

16. Example prompts and templates (ready-to-use)

17. Mini project idea: build a QnA assistant using RAG

1. Ingest product manuals and FAQs, chunk with overlap
2. Create embeddings using a sentence encoder
3. Index vectors in HNSW or FAISS
4. On user query: retrieve top passages, compose prompt with retrieved text, ask GPT to answer and provide citations
5. Evaluate precision, answer length, and latency

18. Common pitfalls and how to avoid them

• Over-reliance on model without retrieval leads to hallucination
• Excessively long prompts exceed context window—truncate intelligently
• Unvetted pretraining data may leak copyrighted or private content
• Blind parameter tuning (temperature, top_p) without human checks can produce harmful outputs

19. Ethical considerations and responsible use

Generative text can amplify bias, create convincing disinformation, and be misused. Best practices:

• Bias audits and fairness checks
• Human review on sensitive outputs
• Disclosure when content is AI-generated
• Rate limits and access controls to prevent abuse

20. Final thoughts and next steps

Text generation with GPT models is a mature but rapidly evolving field. Combining strong prompt engineering, grounding via retrieval, cautious fine-tuning and robust safety systems produces useful and reliable features. For hands-on mastery: build small projects—summarizers, chatbots, and RAG-based assistants—while instrumenting evaluation and safety from day one.

© Content — Text Generation with GPT Models guide. Preserve prompt and model versioning for reproducibility and safety.